Abstract This paper proposes a stochastic multi-objective optimization framework to design real-time pricing for transactive energy. The energy transaction among the load-serving entity and customers is formulated as a bi-level optimization in which load-serving entity acts as the leader in the upper-level of optimization aiming to profit and reserve maximization. To make the interaction between opposite objectives, the modified fuzzy method is employed. The load-serving entity takes part in the wholesale market and purchases energy from the wholesale market and sells it to the customers. The load-serving entity itself possesses some dispatchable resources and it can also buy energy from privately-owned renewable resources. On the other hand, demand response providers are buying energy from the load-serving entity on behalf of consumers. The demand response providers are the followers of the proposed model and seek to maximize the profit of their customers. The communication between LSE and customers can be provided by the energy internet. Using the energy internet, the LSE controls and monitors the behavior of costumers to design real-time prices. The proposed model is tested on a standard case study, and the results show that the reservation and energy not supplied have been improved as 12% and 43.8%, respectively.

中文翻译：

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为动态需求响应程序设计最优交易定价的随机多目标优化：一种双层模糊方法

摘要 本文提出了一个随机多目标优化框架来设计交易能源的实时定价。负载服务实体与客户之间的能源交易被表述为双层优化，其中负载服务实体作为上层优化的领导者，旨在实现利润和储备最大化。为了使对立目标之间产生交互作用，采用了改进的模糊方法。负荷服务实体参与批发市场，从批发市场购买能源并销售给客户。负载服务实体本身拥有一些可调度的资源，它也可以从私有的可再生资源中购买能源。另一方面，需求响应提供商代表消费者从负载服务实体购买能源。需求响应提供者是所提议模型的追随者，并寻求最大化其客户的利润。LSE 与客户之间的通信可以通过能源互联网提供。LSE 使用能源互联网控制和监控客户的行为以设计实时价格。所提出的模型在标准案例研究中进行了测试，结果表明预留和未提供能源分别提高了 12% 和 43.8%。